def test_shape_mapping():
index_map = IndexMap.from_func(lambda i: [i // 4, i % 4])
assert_structural_equal(index_map.map_shape([4]), [1, 4])
assert_structural_equal(index_map.map_shape([16]), [4, 4])
assert_structural_equal(index_map.map_shape([14]), [4, 4])
def test_index_map_inverse_no_iter():
def input_example(i0, i1, i2, i3):
j0 = floordiv(i3, 32)
j1 = floordiv(i2, 2)
j2 = floormod(i2, 2)
j3 = floormod(i3, 32)
return j0, j1, j2, j3
def expected_inverse(i0, i1, i2, i3):
return IntImm("int32", 0), IntImm("int32",
0), i2 + i1 * 2, i3 + i0 * 32
index_map = IndexMap.from_func(input_example)
inverse_map = index_map.inverse([1, 1, 64, 64])
expected_map = IndexMap.from_func(expected_inverse)
assert expected_map.is_equivalent_to(inverse_map)
def test_index_mapping():
index_map = IndexMap.from_func(lambda i: [i // 4, i % 4])
assert_structural_equal(index_map.map_indices([0]), [0, 0])
assert_structural_equal(index_map.map_indices([3]), [0, 3])
assert_structural_equal(index_map.map_indices([4]), [1, 0])
assert_structural_equal(index_map.map_indices([42]), [10, 2])
def check_index_map(workload, block_name, intrin_name, expected_index_map):
s = Schedule(workload)
block = s.get_block(block_name)
desc_func = TensorIntrin.get(intrin_name).desc
info = get_auto_tensorize_mapping_info(s, block, desc_func)
assert len(info.mappings) == 1
assert IndexMap.from_func(expected_index_map).is_equivalent_to(
info.mappings[0])
def assert_equal_index_map(map1: IndexMap, map2: IndexMap) -> None:
iters_1 = map1.map_indices(map2.initial_indices)
iters_2 = map2.final_indices
assert len(iters_1) == len(iters_2)
analyzer = tvm.arith.Analyzer()
for iter1, iter2 in zip(iters_1, iters_2):
assert analyzer.can_prove_equal(iter1, iter2)
def test_nonsurjective_inverse(padding_test_case):
index_map = IndexMap.from_func(padding_test_case["forward"])
inverse, padding_predicate = index_map.non_surjective_inverse(
padding_test_case["pre_shape"])
expected_inverse = IndexMap.from_func(padding_test_case["inverse"])
assert inverse.is_equivalent_to(expected_inverse)
post_shape = index_map.map_shape(padding_test_case["pre_shape"])
tvm.ir.assert_structural_equal(post_shape, padding_test_case["post_shape"])
expected_predicate = padding_test_case["padding"](*inverse.initial_indices)
# Can't use analyzer.can_prove_equal, because it can't simplify
# expressions like `(4*i+j >= 14) - (4*i+j >= 14)`.
analyzer = tvm.arith.Analyzer()
expected_predicate = analyzer.simplify(expected_predicate)
padding_predicate = analyzer.simplify(padding_predicate)
tvm.ir.assert_structural_equal(padding_predicate, expected_predicate)
def test_suggest_index_map_bijective():
i, j = _make_vars("i", "j")
index_map = suggest_index_map(
buffer=decl_buffer(shape=[8]),
indices=[floormod(j, 4) * 2 + i],
loops=_make_loops(
loop_vars=[i, j],
extents=[2, 32],
),
predicate=True,
)
expected_index_map = IndexMap.from_func(
lambda x: [
floormod(x, 2),
floordiv(x, 2),
], )
assert index_map.is_equivalent_to(expected_index_map)
def test_suggest_index_map_simple():
i, j = _make_vars("i", "j")
index_map = suggest_index_map(
buffer=decl_buffer(shape=[8, 256]),
indices=[
floordiv(i, 16) * 4 + floordiv(j, 16),
floormod(i, 16) * 16 + floormod(j, 16),
],
loops=_make_loops(
loop_vars=[i, j],
extents=[32, 64],
),
predicate=True,
)
expected_index_map = IndexMap.from_func(
lambda x, y: [
floordiv(x, 4),
floordiv(y, 16),
floormod(x, 4),
floormod(y, 16),
],
)
assert index_map.is_equivalent_to(expected_index_map)
def test_nonbijective_inverse_gives_error():
index_map = IndexMap.from_func(lambda i: [i // 4, i % 4])
with pytest.raises(tvm.TVMError):
index_map.inverse([14])
def test_inverse():
index_map = IndexMap.from_func(lambda i: [i // 4, i % 4])
expected_inverse = IndexMap.from_func(lambda i, j: [4 * i + j])
assert index_map.inverse([16]).is_equivalent_to(expected_inverse)
def transform_layout(self,
mapping_function: Callable[...,
List[tvm.tir.PrimExpr]]):
"""Defines the layout transformation for the current stage's tensor.
The map from initial_indices to final_indices must be an
invertible affine transformation. This method may be called
more than once for a given tensor, in which case each
transformation is applied sequentially.
If the stage is a ComputeOp, then the iteration order of the
compute stage is rewritten to be a row-major traversal of the
tensor, and the new loop iteration variables are returned.
For all other stages, the loop iteration order is unmodified,
and the return value is None.
Parameters
----------
mapping_function : Callable[..., List[tvm.tir.PrimExpr]]
A callable that accepts N arguments of type tvm.tir.Var,
and outputs a list of PrimExpr. The input arguments
represent the location of a value in the current stage's
tensor, using the pre-transformation layout. The return
value of the function gives the location of that value in
the current stage's tensor, using the post-transformation
layout.
Returns
-------
new_iter_vars : Optional[List[tvm.tir.IterVar]]
If the stage is a ComputeOp, then the return will be the
updated loop iteration variables over the data array, in
the same order as the output values from the
`mapping_function`.
Otherwise, the return value is None.
Examples
--------
.. code-block:: python
# ``A`` is a tensor whose compute definition is in NHWC
# format, and should be transformed into NCHWc format.
s[A].transform_layout(
lambda n,h,w,c: [n, c//4, h, w, c%4]
)
.. code-block:: python
# ``A`` is a tensor whose compute definition is in an
# arbitrary format, and should be transformed such that
# the last index is split, with the slower-changing index
# of the split placed at the slowest changing dimension.
s[A].transform_layout(
lambda *indices, i: [i//4, *indices, i%4]
)
.. code-block:: python
# ``B`` is a tensor defined by te.compute to be a copy of
# ``A`, and should be transformed such that ``B``'s layout
# is a transpose of ``A``'s layout. The loop iteration
# that computes ``B`` will correspond to ``B``'s memory
# layout.
A = te.placeholder([n,m])
B = te.compute(A.shape, lambda i,j: A[i,j])
s = te.create_schedule(B.op)
s[B].transform_layout(lambda i,j: [j,i])
"""
ndim = len(self.op.output(0).shape)
index_map, axis_separators = IndexMap.from_func_with_separators(
mapping_function, ndim=ndim)
new_iter_vars = _ffi_api.StageTransformLayout(
self, index_map.initial_indices, index_map.final_indices)
_ffi_api.StageSetAxisSeparators(self, axis_separators)
return new_iter_vars or None
def _assert_equal_index_map(map1: IndexMap, map2: IndexMap) -> None:
iters_1 = map1.map_indices(map2.initial_indices)
iters_2 = map2.final_indices
assert len(iters_1) == len(iters_2)
for iter1, iter2 in zip(iters_1, iters_2):
assert expr_deep_equal(iter1, iter2)